@article{fdi:010079029,
  title = {{C}orrecting the effect of sampling bias in species distribution modeling : a new method in the case of a low number of presence data},
  author = {{M}oua, {Y}. and {R}oux, {E}mmanuel and {S}eyler, {F}r{\'e}d{\'e}rique and {B}riolant, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}pecies distribution models that only require presence data provide potentially inaccurate results due to sampling bias and presence data scarcity. {M}ethods have been proposed in the literature to minimize the effects of sampling bias, but without explicitly considering the issue of sample size. {A} new method developed to better take into account environmental biases in a context of data scarcity is proposed here. {I}t is compared to other sampling bias correction methods primarily used in the literature by analyzing their absolute and relative impacts on model performances. {R}esults showed that the number of presence sites is critical for selecting the applicable method. {T}he method proposed was regularly placed in the first or second rank and tends to be more proficient than other methods in the context of presence site scarcity (<100). {I}t tends to improve results regarding environment-based performance indexes. {E}ventually, its parametrization, requiring background knowledge on species bio-ecology, appears to be more robust and convenient to perform than those based on geographical criteria.},
  keywords = {{S}ampling bias ; {D}ata scarcity ; {S}pecies distribution models ; {M}axent ; {GUYANE} {FRANCAISE}},
  booktitle = {},
  journal = {{E}cological {I}nformatics},
  volume = {57},
  numero = {},
  pages = {art. 101086 [10 ]},
  ISSN = {1574-9541},
  year = {2020},
  DOI = {10.1016/j.ecoinf.2020.101086},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079029},
}